(1) Field of the Invention
The present invention relates generally to an active noise cancellation system and in particular to an active noise cancellation system for time-varying signals.
(2) Description of the Prior Art
Various noise control techniques have been utilized to reduce both fluidborne and structureborne noise. Generally, noise control techniques aim at the suppression of noise sources or the reduction of sound propagation and transmission. However, noise source suppression methods involve redesign and modification of the system and thus may effect system performance. Furthermore, noise reduction methods that attenuate sound in the propagation and transmission paths approach the problem indirectly and have proven ineffective.
Another approach, known as the active noise control concept has been developed to reduce or possibly cancel the unwanted sound generated from pressure fluctuations in a fluid medium or vibrations in a structure. This approach is currently being used to cancel steady-state noise signals in piping systems, structural vibration noise in aircraft fuselages and background noise in communication links. The basic concept of active noise control involves the use of interfering waves from another source identical to the noise signal except for being phase shifted by 180.degree. to cancel the sound field of the noise source. These basic concepts of active noise control were first introduced in 1936 by Lueg in U.S. Pat. No. 2,043,413 which is herein incorporated by reference.
More recently, prior art active noise cancellation systems have made use of various filter models in attempting to cancel out a noise source. For example, in steady-state signal systems, filter models of the expected noise signals are easily estimated. The models are then employed in an open-loop system to apply a correction signal to introduce the noise cancellation wave. The noise cancellation wave is shifted by 180.degree. with respect to the predicted noise thereby canceling the noise signal. However, many noise signals are not so easily predicted.
In FIG. 1, a closed-loop approach to active noise cancellation involves a noise source exciting a fluid-filled duct 10. The fluid flow 11 through an orifice 12 includes turbulent flow noise 13 generated by a confined fluid jet 14 which is propagated down the duct 10. Ideally, noise measured at a detector hydrophone 15 could be canceled with a similar signal shifted by 180.degree. and introduced at a cancellation source 17 such as an omni-directional sound projector. Practically though, implementation of a 180.degree. phase shift introduces a time delay that corresponds to a physical distance of sound propagation further down the duct 10. Thus, in addition to the 180.degree. phase shift, the response of the duct 10 from the input hydrophone 15 to the cancellation source 17 must be anticipated.
A convenient method of describing this problem is in terms of the joint process estimation problem, as outlined in FIG. 2. The joint process estimator uses an input vector y(T) to estimate another vector d(T) by passing y(T) through an adaptive filter 20. The adaptive filter response is adjusted such that an error output e(T) is minimized. In this case, d(T) corresponds to the noise present at the cancellation source 17 in FIG. 1, while the vectors y(T) and e(T) correspond to the noise measured at the input and error hydrophones 15 and 19, respectively, which are then used as inputs for adaptive filter 20. The function of adaptive filter 20 is typically accomplished with a processor or controller 30 shown in FIG. 1.
The closed-loop adaptive filter approach is described in detail by Eriksson in U.S. Pat. Nos. 4,677,676 and 4,677,677. In Eriksson, adaptive filter models are used to generate an on-line compensation of the noise signal. The adaptive filter model is employed in a closed-loop system to apply a correction signal to an omni-directional speaker. The speaker introduces the noise cancellation wave into the acoustic system. The filter model in Eriksson adaptively models direct, feedback and error paths on an on-line basis. Recursive least mean squares and least mean squares algorithms are employed in the filters' transfer functions. While these and other closed loop systems adapt to time-varying signals, the response time is on the order of minutes. Thus, time-varying noise signals having much shorter time constants are not effectively canceled by any of the prior art active noise control systems.